Bioinformatics Methods for Interpreting Toxicogenomics Data: The Role of Text-Mining

This chapter concerns the application of bioinformatics methods to the analysis of toxicogenomics data. The chapter starts with an introduction covering how bioinformatics has been applied in toxicogenomics data analysis, and continues with a description of the foundations of a specific bioinformatics method called text-mining. Next, the integration of text-mining with toxicogenomics data analysis methods is described. Four different areas in toxicogenomics where conventional bioinformatics solutions can be assisted by text-mining are described: class discovery and separation, connectivity mapping, mechanistic analysis, and identification of early predictors of toxicity. © 2014 Elsevier Inc. All rights reserved.

[1]  R G Ulrich,et al.  Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. , 2001, Toxicology letters.

[2]  David Milward,et al.  Mining protein-protein interactions from published literature using Linguamatics I2E. , 2009, Methods in molecular biology.

[3]  Russ B. Altman,et al.  Author ' s personal copy Using text to build semantic networks for pharmacogenomics , 2010 .

[4]  Silvio Albertini,et al.  Discriminating Different Classes of Toxicants by Transcript Profiling , 2004, Environmental health perspectives.

[5]  Daniel J. Vis,et al.  T-profiler: scoring the activity of predefined groups of genes using gene expression data , 2005, Nucleic Acids Res..

[6]  Martijn J. Schuemie,et al.  A dictionary to identify small molecules and drugs in free text , 2009, Bioinform..

[7]  H. Ellinger-Ziegelbauer,et al.  Comparison of the expression profiles induced by genotoxic and nongenotoxic carcinogens in rat liver. , 2005, Mutation research.

[8]  Ralf Zimmer,et al.  Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts , 2005, ECCB/JBI.

[9]  M. Millecamps,et al.  Role of NFkB in an Animal Model of Complex Regional Pain Syndrome-type I (CRPS-I) , 2009 .

[10]  Peter A. C. 't Hoen,et al.  Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease , 2008, BMC Bioinformatics.

[11]  David Kipling,et al.  Text-based over-representation analysis of microarray gene lists with annotation bias , 2009, Nucleic acids research.

[12]  W. Alkema,et al.  Literature-based compound profiling: application to toxicogenomics. , 2007, Pharmacogenomics.

[13]  Martijn J. Schuemie,et al.  Peregrine: Lightweight gene name normalization by dictionary lookup , 2007 .

[14]  Alexander R. Pico,et al.  WikiPathways: Pathway Editing for the People , 2008, PLoS biology.

[15]  A. Valencia,et al.  A gene network for navigating the literature , 2004, Nature Genetics.

[16]  M. Schuemie,et al.  Anni 2.0: a multipurpose text-mining tool for the life sciences , 2008, Genome Biology.

[17]  Martijn J. Schuemie,et al.  Novel Protein-Protein Interactions Inferred from Literature Context , 2009, PloS one.

[18]  Barend Mons,et al.  Assignment of protein function and discovery of novel nucleolar proteins based on automatic analysis of MEDLINE , 2007, Proteomics.

[19]  Reinhard Schneider,et al.  Martini: using literature keywords to compare gene sets , 2009, Nucleic acids research.

[20]  Maurice Bouwhuis,et al.  CoPub: a literature-based keyword enrichment tool for microarray data analysis , 2008, Nucleic Acids Res..

[21]  D. Swanson.  Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge , 2015, Perspectives in biology and medicine.

[22]  S. Boyer,et al.  Automatic mining of the literature to generate new hypotheses for the possible link between periodontitis and atherosclerosis: lipopolysaccharide as a case study. , 2007, Journal of clinical periodontology.

[23]  P. Bushel,et al.  The evolution of bioinformatics in toxicology: advancing toxicogenomics. , 2011, Toxicological sciences : an official journal of the Society of Toxicology.

[24]  Kerry Blanchard,et al.  Integration of clinical and gene expression endpoints to explore furan-mediated hepatotoxicity. , 2004, Mutation research.

[25]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[26]  Pierre R. Bushel,et al.  CEBS—Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data , 2007, Nucleic Acids Res..

[27]  Andrey Rzhetsky,et al.  Looking at Cerebellar Malformations through Text-Mined Interactomes of Mice and Humans , 2009, PLoS Comput. Biol..

[28]  Alfonso Valencia,et al.  PLAN2L: a web tool for integrated text mining and literature-based bioentity relation extraction , 2009, Nucleic Acids Res..

[29]  L. Grivell,et al.  Text mining for biology - the way forward: opinions from leading scientists , 2008, Genome Biology.

[30]  Joshua F. Robinson,et al.  Discriminating classes of developmental toxicants using gene expression profiling in the embryonic stem cell test. , 2011, Toxicology letters.

[31]  Joaquín Dopazo,et al.  Functional profiling of microarray experiments using text-mining derived bioentities , 2007, Bioinform..

[32]  H. Tomita,et al.  Relationship between hepatic gene expression profiles and hepatotoxicity in five typical hepatotoxicant-administered rats. , 2005, Toxicological sciences : an official journal of the Society of Toxicology.

[33]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Peter A. C. 't Hoen,et al.  Literature-aided interpretation of gene expression data with the weighted global test , 2011, Briefings Bioinform..

[35]  Pierre R. Bushel,et al.  Computational selection of distinct class- and subclass-specific gene expression signatures , 2002, J. Biomed. Informatics.

[36]  Sophia Ananiadou,et al.  Discovering and visualizing indirect associations between biomedical concepts , 2011, Bioinform..

[37]  Jelle J. Goeman,et al.  A global test for groups of genes: testing association with a clinical outcome , 2004, Bioinform..

[38]  J. Pennings,et al.  Early gene expression changes during embryonic stem cell differentiation into cardiomyocytes and their modulation by monobutyl phthalate. , 2009, Reproductive toxicology.

[39]  Chris T. A. Evelo,et al.  WikiPathways: building research communities on biological pathways , 2011, Nucleic Acids Res..

[40]  Jonathan D. Wren,et al.  Knowledge discovery by automated identification and ranking of implicit relationships , 2004, Bioinform..

[41]  K. Bretonnel Cohen,et al.  Getting Started in Text Mining , 2008, PLoS Comput. Biol..

[42]  Jelle J. Goeman,et al.  Testing association of a pathway with survival using gene expression data , 2005, Bioinform..

[43]  Marc Weeber,et al.  Case Report: Generating Hypotheses by Discovering Implicit Associations in the Literature: A Case Report of a Search for New Potential Therapeutic Uses for Thalidomide , 2003, J. Am. Medical Informatics Assoc..

[44]  Yukiko Matsuoka,et al.  PathText: a text mining integrator for biological pathway visualizations , 2010, Bioinform..

[45]  D. Swanson.  Migraine and Magnesium: Eleven Neglected Connections , 2015, Perspectives in biology and medicine.

[46]  Jacob de Vlieg,et al.  Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases , 2010, PLoS Comput. Biol..

[47]  M. Fielden,et al.  A Gene Expression Signature that Predicts the Future Onset of Drug-Induced Renal Tubular Toxicity , 2005, Toxicologic pathology.

[48]  Joyce A. Mitchell,et al.  Using literature-based discovery to identify disease candidate genes , 2005, Int. J. Medical Informatics.

[49]  Zhao-Bang Zeng,et al.  Genes related to apoptosis predict necrosis of the liver as a phenotype observed in rats exposed to a compendium of hepatotoxicants , 2008, BMC Genomics.

[50]  T. Jenssen,et al.  A literature network of human genes for high-throughput analysis of gene expression , 2001 .

[51]  Wanda Pratt,et al.  Using statistical and knowledge-based approaches for literature-based discovery , 2006, J. Biomed. Informatics.

[52]  Michael Krauthammer,et al.  GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data , 2004, J. Biomed. Informatics.

[53]  Martijn J. Schuemie,et al.  Literature-based concept profiles for gene annotation: The issue of weighting , 2008, Int. J. Medical Informatics.

[54]  Carol Friedman,et al.  Exploiting Semantic Relations for Literature-Based Discovery , 2006, AMIA.

[55]  Scott Boyer,et al.  Applied information retrieval and multidisciplinary research: new mechanistic hypotheses in Complex Regional Pain Syndrome , 2007, Journal of biomedical discovery and collaboration.

[56]  Egon L. Willighagen,et al.  Linked open drug data for pharmaceutical research and development , 2011, J. Cheminformatics.

[57]  Rob Jelier,et al.  CoPub Mapper: mining MEDLINE based on search term co-publication , 2005, BMC Bioinformatics.

[58]  Miguel A. Andrade-Navarro,et al.  LAITOR - Literature Assistant for Identification of Terms co-Occurrences and Relationships , 2010, BMC Bioinformatics.

[59]  Hao Chen,et al.  Content-rich biological network constructed by mining PubMed abstracts , 2004, BMC Bioinformatics.

[60]  J. Kleinjans,et al.  Comparison of supervised clustering methods to discriminate genotoxic from non-genotoxic carcinogens by gene expression profiling. , 2005, Mutation research.

[61]  Min Zhang,et al.  A decade of toxicogenomic research and its contribution to toxicological science. , 2012, Toxicological sciences : an official journal of the Society of Toxicology.

[62]  A. Butte,et al.  Open Access Research Article Predicting Environmental Chemical Factors Associated with Disease-related Gene Expression Data , 2022 .

[63]  Russ B. Altman,et al.  Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text , 2009, BMC Bioinformatics.

[64]  K. Bretonnel Cohen,et al.  Manual curation is not sufficient for annotation of genomic databases , 2007, ISMB/ECCB.

[65]  Aldert H Piersma,et al.  Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data , 2013, BMC Medical Genomics.

[66]  Mitchell R. McGill,et al.  Oxidant stress, mitochondria, and cell death mechanisms in drug-induced liver injury: Lessons learned from acetaminophen hepatotoxicity , 2012, Drug metabolism reviews.

[67]  H. V. Jagadish,et al.  ConceptGen: a gene set enrichment and gene set relation mapping tool , 2010, Bioinform..